I expected, that I will get the same prediction, if I multiply the
weights for all classes with a constant factor, but I got different
results. Please look for the following code.
> library(e1071)
> data(Glass, package = "mlbench")
> index <- 1:nrow(Glass)
> testindex <- sample(index, trunc(length(index)/5))
> testset <- Glass[testindex, ]
> trainset <- Glass[-testindex, ]
> datatrain <- subset(trainset,select=-Type)
> classestrain <- subset(trainset,select=Type)
> Wts <- 1.0/table(Glass$Type);
> model <-
svm(datatrain,classestrain,type="C-classification",kernel="radial",cost=100,class.weights=Wts)
> datatest = subset(testset,select=-Type)
> classestest <- subset(testset,select=Type)
> pred.test=predict(model,datatest)
> table.test <- table(pred.test,t(classestest))
> table.test
pred.test 1 2 3 5 6 7
1 11 2 0 0 0 0
2 1 12 0 0 0 0
3 1 3 1 0 0 0
5 0 0 0 1 0 0
6 0 1 0 0 1 0
7 0 1 0 1 0 6
That is the first prediction. Now I multiply all weights by 100, and
create a new model and make the next prediction:
> Wts <- 100.0/table(Glass$Type);
> model <-
svm(datatrain,classestrain,type="C-classification",kernel="radial",cost=100,class.weights=Wts)> pred.test=predict(model,datatest)
> table.test <- table(pred.test,t(classestest))
> table.test
pred.test 1 2 3 5 6 7
1 10 5 0 0 0 0
2 2 13 0 0 0 0
3 1 1 1 0 0 0
5 0 0 0 1 0 0
6 0 0 0 0 1 0
7 0 0 0 1 0 6
This prediction differs from the last prediction. Is this a numerical
inaccuracy or is this a expected result?
Then I observed, if I take the larger weights, but divide the cost by
100, I get again the first prediction:
> model <-
svm(datatrain,classestrain,type="C-classification",kernel="radial",cost=1,class.weights=Wts)> pred.test=predict(model,datatest)
> table.test <- table(pred.test,t(classestest))
> table.test
pred.test 1 2 3 5 6 7
1 11 2 0 0 0 0
2 1 12 0 0 0 0
3 1 3 1 0 0 0
5 0 0 0 1 0 0
6 0 1 0 0 1 0
7 0 1 0 1 0 6
Is this accidentally or an expected result?
